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Tracking changes in soil organic carbon across the heterogeneous agricultural landscape of the Lower Fraser Valley of British Columbia.
Paul, S S; Dowell, L; Coops, N C; Johnson, M S; Krzic, M; Geesing, D; Smukler, S M.
Afiliação
  • Paul SS; Soil Science Program, Faculty of Land and Food Systems, University of British Columbia, 2357 Main Mall, Vancouver, BC V6T 1Z4, Canada. Electronic address: siddpaul@mail.ubc.ca.
  • Dowell L; Soil Science Program, Faculty of Land and Food Systems, University of British Columbia, 2357 Main Mall, Vancouver, BC V6T 1Z4, Canada.
  • Coops NC; Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, BC V6T 1Z4, Canada.
  • Johnson MS; Institute for Resources, Environment and Sustainability, University of British Columbia, 2202 Main Mall, Vancouver, BC V6T 1Z4, Canada.
  • Krzic M; Soil Science Program, Faculty of Land and Food Systems, University of British Columbia, 2357 Main Mall, Vancouver, BC V6T 1Z4, Canada; Department of Forest and Conservation Sciences, University of British Columbia, 2357 Main Mall, Vancouver, BC V6T 1Z4, Canada.
  • Geesing D; Ministry of Agriculture, Government of British Columbia, 1767 Angus Campbell Rd, Abbotsford, BC V3G 2M3, Canada.
  • Smukler SM; Soil Science Program, Faculty of Land and Food Systems, University of British Columbia, 2357 Main Mall, Vancouver, BC V6T 1Z4, Canada.
Sci Total Environ ; 732: 138994, 2020 Aug 25.
Article em En | MEDLINE | ID: mdl-32438157
Increasing soil organic carbon (SOC) can improve the capacity of agricultural systems to both adapt to and mitigate climate change. Despite its importance, the current understanding of the magnitude or even the direction of SOC change in agricultural landscapes is limited. While changes in land use/land cover (LULC) and climate are among the main drivers of changes in SOC, their relative importance for the spatiotemporal assessment of SOC is unclear. This study evaluated LULC and SOC dynamics using archived and recent soil samples, remote sensing, and digital soil mapping in the Lower Fraser Valley of British Columbia, Canada. We combined both pixel- and object-based analysis of Landsat satellite imagery to assess LULC changes from 1984 to 2018. We achieved an overall accuracy of 81% and kappa coefficient of 0.77 for LULC classification using a random forest model. For predicting SOC for the same time period, we applied soil and vegetation indices derived from Landsat images, topographic indices, historic soil survey variables, and climate data in a random forest model. The SOC prediction of 2018 resulted in a coefficient of determination (R2) of 0.67, concordance correlation coefficient (CCC) of 0.76, and normalized root mean square error (nRMSE) of 0.12. For 1984, the SOC prediction accuracies were 0.46, 0.58, and 0.18 for R2, CCC, and nRMSE, respectively. We detected SOC loss in 61%, gain in 12%, while 27% remained unchanged across the study area. Although we detected large losses of SOC due to LULC change, the majority of the SOC losses across the landscape were attributed to areas that were remained in the same type of agricultural production since 1984. Climate variability did not, however, have a strong effect on SOC changes. These results can inform decision making in the study area to support sustainable LULC management for enhancing SOC sequestration.
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Texto completo: 1 Bases de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sci Total Environ Ano de publicação: 2020 Tipo de documento: Article

Texto completo: 1 Bases de dados: MEDLINE Tipo de estudo: Prognostic_studies Idioma: En Revista: Sci Total Environ Ano de publicação: 2020 Tipo de documento: Article